As data of an unprecedented scale are becoming accessible on the Web, personalization, of narrowing down the retrieval to meet the user-specific information needs, is becoming more and more critical. For instance, in the context of text retrieval, in contrast to traditional web search engines retrieving the same results for all users, major commercial search engines are starting to support personalization, improving the search quality by adapting to the user-specific retrieval contexts, e.g., prior search history or other application contexts. This paper studies how to enable such personalization in the context of structured data retrieval. In particular, we adopt context-sensitive ranking model to formalize personalization as a cost-based optimization over context-sensitive rankings collected. With this formalism, personalization is essentially retrieving the context-sensitive ranking matching the specific user's retrieval context and generating a personalized ranking accordingly. In particular, we adopt a machine learning approach, to effectively and efficiently identify the ideal personalized ranked results for this specific user. Our empirical evaluations over real-life data validate both the effectiveness and efficiency of our framework.
|Title of host publication||Proceedings of the 2007 ACM Symposium on Applied Computing|
|Publisher||Association for Computing Machinery|
|Number of pages||5|
|ISBN (Print)||1595934804, 9781595934802|
|Publication status||Published - 2007|
|Event||2007 ACM Symposium on Applied Computing - Seoul, Korea, Republic of|
Duration: 2007 Mar 11 → 2007 Mar 15
|Name||Proceedings of the ACM Symposium on Applied Computing|
|Other||2007 ACM Symposium on Applied Computing|
|Country/Territory||Korea, Republic of|
|Period||07/3/11 → 07/3/15|
Bibliographical noteFunding Information:
The authors are grateful for the financial support of AstenJohnson Inc. and the Natural Sciences and Engineering Research Council of Canada. All tomographic reconstructions were carried out in Professor Markku Kataja’s laboratory in the Department of Physics, University of Jyväskylä, Finland. The authors are highly appreciative of Professor Kataja’s advice and support.
All Science Journal Classification (ASJC) codes